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Copy pathexperiment_class_shift.py
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183 lines (150 loc) · 7.52 KB
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import os
import argparse
from settings import *
from DataHandling.DataGenerator import build_in_out_generator
from DataHandling.DataGenerator_UCM import generator_ucm, get_ucm_class_splits
from DataHandling.DataGenerator_AID import generator_aid, get_aid_class_splits
from Models.model_prepare import prepare_model
import random
import tensorflow as tf
import numpy as np
def run_experiment(data_set, approach, exp_save_path, seed):
random.seed(seed)
tf.random.set_seed(seed)
np.random.seed(seed)
batch_size = 32
model_type = Models.ResNet50
mu, std = 0, 255
training_fraction = [0.0, 0.7]
validation_fraction = [0.7, 1.0]
num_epochs = 25
band_filter_train_in = band_filter_train_ood = band_filter_val_in = band_filter_val_ood = None
if data_set is Dataset.UCM:
data_root_path = ucm_root_path
input_shape = [256, 256, 3]
crop_shape = [241, 241, 3]
resize_shape = [256, 256, 3]
generator = generator_ucm
classes_in, classes_out_training, _ = get_ucm_class_splits()
elif data_set is Dataset.AID:
data_root_path = aid_root_path
input_shape = [600, 600, 3]
resize_shape = [256, 256, 3]
crop_shape = [500, 500, 3]
generator = generator_aid
classes_in, classes_out_training, _ = get_aid_class_splits()
num_classes = len(classes_in)
if approach is Approaches.dpn_rs or approach is Approaches.dpn_plus:
beta_in=2
beta_out_in=0
beta_out_out=1.0 / num_classes
elif approach is Approaches.prior_kl_forward or approach is Approaches.prior_kl_reverse:
beta_in=100
beta_out_in=1
beta_out_out=1
elif approach is Approaches.enn_cross_entropy:
beta_in=2
beta_out_in=0
beta_out_out=None
# Create data Loader
gen_in_train, gen_in_train_steps = generator(root_folder=data_root_path,
batch_size=batch_size,
set_fraction=training_fraction,
filter_classes=classes_in,
band_filter=band_filter_train_in,
seed=seed)
if approach is Approaches.enn_cross_entropy:
gen_out_train = None
else:
gen_out_train, _ = generator(root_folder=data_root_path,
batch_size=batch_size,
set_fraction=training_fraction,
filter_classes=classes_out_training,
band_filter=band_filter_train_ood,
seed=seed)
train_flow = build_in_out_generator(gen_in=gen_in_train,
gen_ood=gen_out_train,
batch_size=batch_size,
input_shape=input_shape,
crop_shape=crop_shape,
resize_shape=resize_shape,
vertical_flip=True,
horizontal_flip=True,
norm_mu=mu,
norm_std=std,
output_shape=[num_classes],
beta_in=beta_in,
beta_out_in=beta_out_in,
beta_out_out=beta_out_out)
gen_in_val, gen_in_val_steps = generator(root_folder=data_root_path,
batch_size=batch_size,
set_fraction=validation_fraction,
filter_classes=classes_in,
band_filter=band_filter_val_in,
seed=seed)
if approach is Approaches.enn_cross_entropy:
gen_out_val = None
else:
gen_out_val, _ = generator(root_folder=data_root_path,
batch_size=batch_size,
set_fraction=validation_fraction,
filter_classes=classes_out_training,
band_filter=band_filter_val_ood,
seed=seed)
val_flow = build_in_out_generator(gen_in=gen_in_val,
gen_ood=gen_out_val,
batch_size=batch_size,
input_shape=input_shape,
crop_shape=crop_shape,
resize_shape=resize_shape,
vertical_flip=True,
horizontal_flip=True,
norm_mu=mu,
norm_std=std,
output_shape=[num_classes],
beta_in=beta_in,
beta_out_in=beta_out_in,
beta_out_out=beta_out_out)
# Build model and callbacks
model, callbacks = prepare_model(model_type=model_type,
approach=approach,
num_classes=num_classes,
input_shape=resize_shape)
model.summary()
# Start training process
model.fit(train_flow,
steps_per_epoch=gen_in_train_steps,
validation_data=val_flow,
validation_steps=gen_in_val_steps,
epochs=num_epochs,
max_queue_size=100,
callbacks=[callbacks],
verbose=1)
model.save_weights(os.path.join(exp_save_path, "final_model"))
if __name__=="__main__":
parser = argparse.ArgumentParser(
description='Foo')
parser.add_argument('-d','--data', help='Name of data set. [ucm, aid]', type=str, required=True)
parser.add_argument('-a', '--approach', help=f"Approach use for ood detection. {['dpn_rs', 'prior_forward', 'prior_reverse', 'dpn_plus', 'evidential_cross_entropy']}", type=str, default="dpn_rs")
parser.add_argument('-s','--seed', help='Output file name.', type=int, default=42)
parser.add_argument('-p', '--path', help='Path for saving results.', type=str, default='./')
args = parser.parse_args()
assert args.approach in ["dpn_rs", "prior_kl_forward", "prior_kl_reverse", "dpn_plus", "evidential_cross_entropy"], f"approach '{args.approach}' not valid argument!"
if args.approach == "dpn_rs":
approach = Approaches.dpn_rs
elif args.approach == "prior_kl_forward":
approach = Approaches.prior_kl_forward
elif args.approach == "prior_kl_reverse":
approach = Approaches.prior_kl_reverse
elif args.approach == "dpn_plus":
approach = Approaches.dpn_plus
elif args.approach == "evidential_cross_entropy":
approach = Approaches.enn_cross_entropy
assert args.data in ["ucm", "aid"], f"Data set '{args.data}' is not a valid option (aid / ucm)."
if args.data == "ucm":
data_set = Dataset.UCM
elif args.data == "aid":
data_set = Dataset.AID
save_path = args.path
seed = args.seed
run_experiment(data_set=data_set, approach=approach, exp_save_path=save_path, seed=seed)